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51. Lecture 12. H) Equivalence Between LRT and Wald Tests‏‎ (used on 1 page)
52. Lecture 14. G) Somewhat Pedantic Remark on Notation‏‎ (used on 1 page)
53. Lecture 17. C) Asymptotic Properties of OLS‏‎ (used on 1 page)
54. Lecture 4. F) Gamma‏‎ (used on 1 page)
55. Lecture 7. D) Statistical Inference‏‎ (used on 1 page)
56. Lecture 10. C) Cramer-Rao Lower Bound‏‎ (used on 1 page)
57. Lecture 12. I) Optimal Tests‏‎ (used on 1 page)
58. Lecture 15. A) Asymptotic Properties of ML Estimators‏‎ (used on 1 page)
59. Lecture 17. D) Bootstrapping‏‎ (used on 1 page)
60. Lecture 4. G) Normal‏‎ (used on 1 page)
61. Lecture 8. A) Point Estimation‏‎ (used on 1 page)
62. Lecture 11. A) Hypothesis Testing‏‎ (used on 1 page)
63. Lecture 12. J) Neyman-Pearson Lemma‏‎ (used on 1 page)
64. Lecture 15. B) Some Implications‏‎ (used on 1 page)
65. Lecture 18. A) Multicollinearity‏‎ (used on 1 page)
66. Lecture 4. H) Dirac delta function‏‎ (used on 1 page)
67. Lecture 8. B) Method of Moments‏‎ (used on 1 page)
68. Lecture 11. B) Testing Procedure‏‎ (used on 1 page)
69. Lecture 13. A) Test Optimality (cont.)‏‎ (used on 1 page)
70. Lecture 15. C) Example: Hypothesis Test‏‎ (used on 1 page)
71. Lecture 18. B) Partitioned Regression‏‎ (used on 1 page)
72. Lecture 5. A) Families of Distributions‏‎ (used on 1 page)
73. Lecture 8. C) Maximum Likelihood‏‎ (used on 1 page)
74. Lecture 11. C) Variation on a Theme‏‎ (used on 1 page)
75. Lecture 13. B) Example: Normal‏‎ (used on 1 page)
76. Lecture 15. D) Example: Exponential Distribution‏‎ (used on 1 page)
77. Lecture 18. C) Gauss-Markov Theorem‏‎ (used on 1 page)
78. Lecture 5. B) Chebychev's Inequality‏‎ (used on 1 page)
79. Lecture 9. A) Point Estimation (cont.)‏‎ (used on 1 page)
80. Lecture 11. D) Testing Errors‏‎ (used on 1 page)
81. Lecture 13. C) Karlin-Rubin Theorem‏‎ (used on 1 page)
82. Lecture 15. E) Multiple Parameters‏‎ (used on 1 page)
83. Lecture 2. A) Random Variables (cont.)‏‎ (used on 1 page)
84. Lecture 5. C) Multiple Random Variables‏‎ (used on 1 page)
85. Lecture 9. B) Evaluating Estimators‏‎ (used on 1 page)
86. Lecture 11. E) Power Function‏‎ (used on 1 page)
87. Lecture 13. D) 2-sided Tests and Unbiased Tests‏‎ (used on 1 page)
88. Lecture 16. A) Bayesian Inference‏‎ (used on 1 page)
89. Lecture 2. B) Leibniz Rule‏‎ (used on 1 page)
90. Lecture 6. A) Multiple Random Variables (cont.)‏‎ (used on 1 page)
91. Lecture 9. C) Minimum Variance Estimators‏‎ (used on 1 page)
92. Lecture 11. F) Example 1‏‎ (used on 1 page)
93. Lecture 13. E) p-value‏‎ (used on 1 page)
94. Lecture 16. B) Example: Coin Tossing‏‎ (used on 1 page)
95. Lecture 2. C) Transformations of Random Variables‏‎ (used on 1 page)
96. Lecture 6. B) Conditional PMF/PDF‏‎ (used on 1 page)
97. Lecture 9. D) Sufficient Statistics‏‎ (used on 1 page)

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